The Paleocene-Eocene Thermal Maximum(PETM) event was a dramatic global warming w55.93 Ma ago that resulted in biological extinction events, lithological changes, and major deviations in σ13 C and σ18 O.The southwest...The Paleocene-Eocene Thermal Maximum(PETM) event was a dramatic global warming w55.93 Ma ago that resulted in biological extinction events, lithological changes, and major deviations in σ13 C and σ18 O.The southwestern Tarim Basin of China exposes successive Paleogene strata as a result of Tethys evolution and is considered an ideal region for PETM research.Based on calcareous nannoplankton biostratigraphy, we also used stable isotopes and XRD to analyse the Paleocene-Eocene transition in the Tarim Basin. At the Bashibulake Section, the PETM interval is characterized by(1) an abrupt negative shifts in σ13 C_(org), σ13 C_(carb) and σ18 O(-3%, -4.5% and -3%respectively);(2) an obvious negative correlation between the K-mode(Discoaster, Fasciculithus, Ericsonia, Sphenolithus and Rhomboaster) and r-mode(Biscutum, Chiasmolithus, Toweius) nannofossil taxa coincident with a robust Rhomboaster-Discoaster assemblage; and(3) a significant increase in the percentage of detrital input along with an increase in gypsum content. In the upper part of the Qimugen Formation Micrantholithus and Braarudosphaera are commonly found right up to the top where most of the nannofloras suffer a sharp decrease. In the overlying Gaijitage Formation, calcareous nannofossils disappear completely. These events indicate that the southwestern Tarim Basin was a warm shallow continental shelf during the deposition of the Qimugen Formation. From the early Eocene, the environment changed conspicuously. Evaporation increased and sea level fell, which led to an acid climate.This climate mode continued within the youngest unit studied, the Gaijitage Formation, characterized by the deposition of thick evaporates. Consequently, most of the marine plankton, i.e. calcareous nannoplankton, became disappear, because of the significant climate shift.展开更多
The derivative expressions between activation energy (E) and the temperature at the maximum mass loss rate(Tmax) and between activation energy (E) and exponent (N) were deduced in the light of Arrhenius theory. It was...The derivative expressions between activation energy (E) and the temperature at the maximum mass loss rate(Tmax) and between activation energy (E) and exponent (N) were deduced in the light of Arrhenius theory. It was found that the increase of activation energy results in the decrease of exponent and the increase of Tmax. The kinetic parameters were involved in the analysis of the thermal degradation of several polymers. The degradation kinetics of these polymers well complied with the prediction of the derivative expressions for the polymer degradation with single mechanism dominated.展开更多
Thermal image, or thermogram, becomes a new type of signal for machine condition monitoring and fault diagnosis due to the capability to display real-time temperature distribution and possibility to indicate the mach...Thermal image, or thermogram, becomes a new type of signal for machine condition monitoring and fault diagnosis due to the capability to display real-time temperature distribution and possibility to indicate the machine’s operating condition through its temperature. In this paper, an investigation of using the second-order statistical features of thermogram in association with minimum redundancy maximum relevance (mRMR) feature selection and simplified fuzzy ARTMAP (SFAM) classification is conducted for rotating machinery fault diagnosis. The thermograms of different machine conditions are firstly preprocessed for improving the image contrast, removing noise, and cropping to obtain the regions of interest (ROIs). Then, an enhanced algorithm based on bi-dimensional empirical mode decomposition is implemented to further increase the quality of ROIs before the second-order statistical features are extracted from their gray-level co-occurrence matrix (GLCM). The highly relevant features to the machine condition are selected from the total feature set by mRMR and are fed into SFAM to accomplish the fault diagnosis. In order to verify this investigation, the thermograms acquired from different conditions of a fault simulator including normal, misalignment, faulty bearing, and mass unbalance are used. This investigation also provides a comparative study of SFAM and other traditional methods such as back-propagation and probabilistic neural networks. The results show that the second-order statistical features used in this framework can provide a plausible accuracy in fault diagnosis of rotating machinery.展开更多
基金financially supported in part by funds from the State Key Laboratory of Palaeobiology and Stratigraphy (Nanjing Institute of Geology and Palaeontology, CAS) (GBL215010)National Basic Research Program of China (973 Program, No. 2012CB822002)+3 种基金the National Natural Science Foundation of China (Nos. 41302008, 41172037)the Fundamental Research Funds for the Central Universities (53200859490)Science and Technology Innovation Fund of the China University of Geoscience (Beijing)the Beijing Higher Education Young Elite Teacher Project (YETP0665)
文摘The Paleocene-Eocene Thermal Maximum(PETM) event was a dramatic global warming w55.93 Ma ago that resulted in biological extinction events, lithological changes, and major deviations in σ13 C and σ18 O.The southwestern Tarim Basin of China exposes successive Paleogene strata as a result of Tethys evolution and is considered an ideal region for PETM research.Based on calcareous nannoplankton biostratigraphy, we also used stable isotopes and XRD to analyse the Paleocene-Eocene transition in the Tarim Basin. At the Bashibulake Section, the PETM interval is characterized by(1) an abrupt negative shifts in σ13 C_(org), σ13 C_(carb) and σ18 O(-3%, -4.5% and -3%respectively);(2) an obvious negative correlation between the K-mode(Discoaster, Fasciculithus, Ericsonia, Sphenolithus and Rhomboaster) and r-mode(Biscutum, Chiasmolithus, Toweius) nannofossil taxa coincident with a robust Rhomboaster-Discoaster assemblage; and(3) a significant increase in the percentage of detrital input along with an increase in gypsum content. In the upper part of the Qimugen Formation Micrantholithus and Braarudosphaera are commonly found right up to the top where most of the nannofloras suffer a sharp decrease. In the overlying Gaijitage Formation, calcareous nannofossils disappear completely. These events indicate that the southwestern Tarim Basin was a warm shallow continental shelf during the deposition of the Qimugen Formation. From the early Eocene, the environment changed conspicuously. Evaporation increased and sea level fell, which led to an acid climate.This climate mode continued within the youngest unit studied, the Gaijitage Formation, characterized by the deposition of thick evaporates. Consequently, most of the marine plankton, i.e. calcareous nannoplankton, became disappear, because of the significant climate shift.
文摘The derivative expressions between activation energy (E) and the temperature at the maximum mass loss rate(Tmax) and between activation energy (E) and exponent (N) were deduced in the light of Arrhenius theory. It was found that the increase of activation energy results in the decrease of exponent and the increase of Tmax. The kinetic parameters were involved in the analysis of the thermal degradation of several polymers. The degradation kinetics of these polymers well complied with the prediction of the derivative expressions for the polymer degradation with single mechanism dominated.
文摘Thermal image, or thermogram, becomes a new type of signal for machine condition monitoring and fault diagnosis due to the capability to display real-time temperature distribution and possibility to indicate the machine’s operating condition through its temperature. In this paper, an investigation of using the second-order statistical features of thermogram in association with minimum redundancy maximum relevance (mRMR) feature selection and simplified fuzzy ARTMAP (SFAM) classification is conducted for rotating machinery fault diagnosis. The thermograms of different machine conditions are firstly preprocessed for improving the image contrast, removing noise, and cropping to obtain the regions of interest (ROIs). Then, an enhanced algorithm based on bi-dimensional empirical mode decomposition is implemented to further increase the quality of ROIs before the second-order statistical features are extracted from their gray-level co-occurrence matrix (GLCM). The highly relevant features to the machine condition are selected from the total feature set by mRMR and are fed into SFAM to accomplish the fault diagnosis. In order to verify this investigation, the thermograms acquired from different conditions of a fault simulator including normal, misalignment, faulty bearing, and mass unbalance are used. This investigation also provides a comparative study of SFAM and other traditional methods such as back-propagation and probabilistic neural networks. The results show that the second-order statistical features used in this framework can provide a plausible accuracy in fault diagnosis of rotating machinery.
